Import data with R - Importing data from the web (Part 2)

From JSON to R

install.packages("jsonlite")

In the simplest setting, fromJSON() can convert character strings that represent JSON data into a nicely structured R list.

# Load the jsonlite package
library(jsonlite)

# wine_json is a JSON
wine_json <- '{"name":"Chateau Migraine", "year":1997, "alcohol_pct":12.4, "color":"red", "awarded":false}'

# Convert wine_json into a list: wine
wine <- fromJSON(wine_json)

# Print structure of wine
str(wine)

## List of 5
##  $ name       : chr "Chateau Migraine"
##  $ year       : int 1997
##  $ alcohol_pct: num 12.4
##  $ color      : chr "red"
##  $ awarded    : logi FALSE

Quandl API

fromJSON() also works if you pass a URL as a character string or the path to a local file that contains JSON data. Let’s try this out on the Quandl API, where you can fetch all sorts of financial and economical data.

# Definition of quandl_url
quandl_url <- "https://www.quandl.com/api/v3/datasets/WIKI/FB/data.json?auth_token=i83asDsiWUUyfoypkgMz"

# Import Quandl data: quandl_data
quandl_data <- fromJSON(quandl_url)

# Print structure of quandl_data
str(quandl_data)

## List of 1
##  $ dataset_data:List of 10
##   ..$ limit       : NULL
##   ..$ transform   : NULL
##   ..$ column_index: NULL
##   ..$ column_names: chr [1:13] "Date" "Open" "High" "Low" ...
##   ..$ start_date  : chr "2012-05-18"
##   ..$ end_date    : chr "2018-03-27"
##   ..$ frequency   : chr "daily"
##   ..$ data        : chr [1:1472, 1:13] "2018-03-27" "2018-03-26" "2018-03-23" "2018-03-22" ...
##   ..$ collapse    : NULL
##   ..$ order       : NULL

We successfully imported JSON data directly from the web. If you have a close look at the structure of quandl_data, you’ll see that the data element is a matrix.

OMDb API

We saw how easy it is to interact with an API once you know how to formulate requests. You also saw how to fetch all information on Rain Man from OMDb. Simply perform a GET() call, and next ask for the contents with the content() function. This content() function, which is part of the httr package, uses jsonlite behind the scenes to import the JSON data into R.

However, by now you also know that jsonlite can handle URLs itself. Simply passing the request URL to fromJSON() will get your data into R. In this exercise, you will be using this technique to compare the release year of two movies in the Open Movie Database.

# Definition of the URLs
url_sw4 <- "http://www.omdbapi.com/?apikey=72bc447a&i=tt0076759&r=json"
url_sw3 <- "http://www.omdbapi.com/?apikey=72bc447a&i=tt0121766&r=json"

# Import two URLs with fromJSON(): sw4 and sw3
sw4 <- fromJSON(url_sw4)
sw3 <- fromJSON(url_sw3)

# Print out the Title element of both lists
sw4$Title

## [1] "Star Wars: Episode IV - A New Hope"

sw3$Title

## [1] "Star Wars: Episode III - Revenge of the Sith"

# Is the release year of sw4 later than sw3?
sw4$Year > sw4$Year

## [1] FALSE

JSON practice (1)

JSON is built on two structures: objects and arrays. To help you experiment with these, two JSON strings are included in the sample code. It’s up to you to change them appropriately and then call jsonlite’s fromJSON() function on them each time.

# Challenge 1
json1 <- '[1, 2, 3, 4, 5, 6]'
fromJSON(json1)

## [1] 1 2 3 4 5 6

# Challenge 2
json2 <- '{"a": [1, 2, 3], "b": [4, 5, 6]}'
fromJSON(json2)

## $a
## [1] 1 2 3
## 
## $b
## [1] 4 5 6

JSON practice (2)

We prepared two more JSON strings in the sample code. Can you change them and call jsonlite’s fromJSON() function on them, similar to the previous exercise?

# Challenge 1
json1 <- '[[1, 2], [3, 4]]'
fromJSON(json1)

##      [,1] [,2]
## [1,]    1    2
## [2,]    3    4

# Challenge 2
json2 <- '[{"a": 1, "b": 2}, {"a": 3, "b": 4} , {"a": 5, "b": 6}]'
fromJSON(json2)

##   a b
## 1 1 2
## 2 3 4
## 3 5 6

As you can see different JSON data structures will lead to different data structures in R

toJSON()

Apart from converting JSON to R with fromJSON(), you can also use toJSON() to convert R data to a JSON format. In its most basic use, you simply pass this function an R object to convert to a JSON. The result is an R object of the class json, which is basically a character string representing that JSON.

For this exercise, you will be working with a .csv file containing information on the amount of desalinated water that is produced around the world. As you’ll see, it contains a lot of missing values. This data can be found on the URL that is specified in the sample code.

# URL pointing to the .csv file
url_csv <- "http://s3.amazonaws.com/assets.datacamp.com/production/course_1478/datasets/water.csv"

# Import the .csv file located at url_csv
water <- read.csv(url_csv, stringsAsFactors = FALSE)

# Convert the data file according to the requirements
water_json <- toJSON(water)

# Print out water_json
water_json

## [{"water":"Algeria","X1992":0.064,"X2002":0.017},{"water":"American Samoa"},{"water":"Angola","X1992":0.0001,"X2002":0.0001},{"water":"Antigua and Barbuda","X1992":0.0033},{"water":"Argentina","X1992":0.0007,"X1997":0.0007,"X2002":0.0007},{"water":"Australia","X1992":0.0298,"X2002":0.0298},{"water":"Austria","X1992":0.0022,"X2002":0.0022},{"water":"Bahamas","X1992":0.0013,"X2002":0.0074},{"water":"Bahrain","X1992":0.0441,"X2002":0.0441,"X2007":0.1024},{"water":"Barbados","X2007":0.0146},{"water":"British Virgin Islands","X2007":0.0042},{"water":"Canada","X1992":0.0027,"X2002":0.0027},{"water":"Cape Verde","X1992":0.002,"X1997":0.0017},{"water":"Cayman Islands","X1992":0.0033},{"water":"Central African Rep."},{"water":"Chile","X1992":0.0048,"X2002":0.0048},{"water":"Colombia","X1992":0.0027,"X2002":0.0027},{"water":"Cuba","X1992":0.0069,"X1997":0.0069,"X2002":0.0069},{"water":"Cyprus","X1992":0.003,"X1997":0.003,"X2002":0.0335},{"water":"Czech Rep.","X1992":0.0002,"X2002":0.0002},{"water":"Denmark","X1992":0.015,"X2002":0.015},{"water":"Djibouti","X1992":0.0001,"X2002":0.0001},{"water":"Ecuador","X1992":0.0022,"X1997":0.0022,"X2002":0.0022},{"water":"Egypt","X1992":0.025,"X1997":0.025,"X2002":0.1},{"water":"El Salvador","X1992":0.0001,"X2002":0.0001},{"water":"Finland","X1992":0.0001,"X2002":0.0001},{"water":"France","X1992":0.0117,"X2002":0.0117},{"water":"Gibraltar","X1992":0.0077},{"water":"Greece","X1992":0.01,"X2002":0.01},{"water":"Honduras","X1992":0.0002,"X2002":0.0002},{"water":"Hungary","X1992":0.0002,"X2002":0.0002},{"water":"India","X1997":0.0005,"X2002":0.0005},{"water":"Indonesia","X1992":0.0187,"X2002":0.0187},{"water":"Iran","X1992":0.003,"X1997":0.003,"X2002":0.003,"X2007":0.2},{"water":"Iraq","X1997":0.0074,"X2002":0.0074},{"water":"Ireland","X1992":0.0002,"X2002":0.0002},{"water":"Israel","X1992":0.0256,"X2002":0.0256,"X2007":0.14},{"water":"Italy","X1992":0.0973,"X2002":0.0973},{"water":"Jamaica","X1992":0.0005,"X1997":0.0005,"X2002":0.0005},{"water":"Japan","X1997":0.04,"X2002":0.04},{"water":"Jordan","X1997":0.002,"X2007":0.0098},{"water":"Kazakhstan","X1997":1.328,"X2002":1.328},{"water":"Kuwait","X1992":0.507,"X1997":0.231,"X2002":0.4202},{"water":"Lebanon","X2007":0.0473},{"water":"Libya","X2002":0.018},{"water":"Malaysia","X1992":0.0043,"X2002":0.0043},{"water":"Maldives","X1992":0.0004},{"water":"Malta","X1992":0.024,"X1997":0.031,"X2002":0.031},{"water":"Marshall Islands","X1992":0.0007},{"water":"Mauritania","X1992":0.002,"X2002":0.002},{"water":"Mexico","X1992":0.0307,"X2002":0.0307},{"water":"Morocco","X1992":0.0034,"X1997":0.0034,"X2002":0.007},{"water":"Namibia","X1992":0.0003,"X2002":0.0003},{"water":"Netherlands Antilles","X1992":0.063},{"water":"Nicaragua","X1992":0.0002,"X2002":0.0002},{"water":"Nigeria","X1992":0.003,"X2002":0.003},{"water":"Norway","X1992":0.0001,"X2002":0.0001},{"water":"Oman","X1997":0.034,"X2002":0.034,"X2007":0.109},{"water":"Peru","X1992":0.0054,"X2002":0.0054},{"water":"Poland","X1992":0.007,"X2002":0.007},{"water":"Portugal","X1992":0.0016,"X2002":0.0016},{"water":"Qatar","X1992":0.065,"X1997":0.099,"X2002":0.099,"X2007":0.18},{"water":"Saudi Arabia","X1992":0.683,"X1997":0.727,"X2002":0.863,"X2007":1.033},{"water":"Senegal","X1992":0,"X2002":0},{"water":"Somalia","X1992":0.0001,"X2002":0.0001},{"water":"South Africa","X1992":0.018,"X2002":0.018},{"water":"Spain","X1992":0.1002,"X2002":0.1002},{"water":"Sudan","X1992":0.0004,"X1997":0.0004,"X2002":0.0004},{"water":"Sweden","X1992":0.0002,"X2002":0.0002},{"water":"Trinidad and Tobago","X2007":0.036},{"water":"Tunisia","X1992":0.008,"X2002":0.013},{"water":"Turkey","X1992":0.0005,"X2002":0.0005,"X2007":0.0005},{"water":"United Arab Emirates","X1992":0.163,"X1997":0.385,"X2007":0.95},{"water":"United Kingdom","X1992":0.0333,"X2002":0.0333},{"water":"United States","X1992":0.58,"X2002":0.58},{"water":"Venezuela","X1992":0.0052,"X2002":0.0052},{"water":"Yemen, Rep.","X1992":0.01,"X2002":0.01}]

As you can see, the JSON you printed out isn’t easy to read. In the next exercise, you will print out some more JSONs, and explore ways to prettify or minify the output

Minify and prettify

JSONs can come in different formats. Take these two JSONs, that are in fact exactly the same: the first one is in a minified format, the second one is in a pretty format with indentation, whitespace and new lines:

# Mini
{"a":1,"b":2,"c":{"x":5,"y":6}}

# Pretty
{
  "a": 1,
  "b": 2,
  "c": {
    "x": 5,
    "y": 6
  }
}

Unless you’re a computer, you surely prefer the second version. However, the standard form that toJSON() returns, is the minified version, as it is more concise. You can adapt this behavior by setting the pretty argument inside toJSON() to TRUE. If you already have a JSON string, you can use prettify() or minify() to make the JSON pretty or as concise as possible.

# Convert mtcars to a pretty JSON: pretty_json
pretty_json <- toJSON(mtcars, pretty=TRUE)

# Print pretty_json
pretty_json

## [
##   {
##     "mpg": 21,
##     "cyl": 6,
##     "disp": 160,
##     "hp": 110,
##     "drat": 3.9,
##     "wt": 2.62,
##     "qsec": 16.46,
##     "vs": 0,
##     "am": 1,
##     "gear": 4,
##     "carb": 4,
##     "_row": "Mazda RX4"
##   },
##   {
##     "mpg": 21,
##     "cyl": 6,
##     "disp": 160,
##     "hp": 110,
##     "drat": 3.9,
##     "wt": 2.875,
##     "qsec": 17.02,
##     "vs": 0,
##     "am": 1,
##     "gear": 4,
##     "carb": 4,
##     "_row": "Mazda RX4 Wag"
##   },
##   {
##     "mpg": 22.8,
##     "cyl": 4,
##     "disp": 108,
##     "hp": 93,
##     "drat": 3.85,
##     "wt": 2.32,
##     "qsec": 18.61,
##     "vs": 1,
##     "am": 1,
##     "gear": 4,
##     "carb": 1,
##     "_row": "Datsun 710"
##   },
##   {
##     "mpg": 21.4,
##     "cyl": 6,
##     "disp": 258,
##     "hp": 110,
##     "drat": 3.08,
##     "wt": 3.215,
##     "qsec": 19.44,
##     "vs": 1,
##     "am": 0,
##     "gear": 3,
##     "carb": 1,
##     "_row": "Hornet 4 Drive"
##   },
##   {
##     "mpg": 18.7,
##     "cyl": 8,
##     "disp": 360,
##     "hp": 175,
##     "drat": 3.15,
##     "wt": 3.44,
##     "qsec": 17.02,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 2,
##     "_row": "Hornet Sportabout"
##   },
##   {
##     "mpg": 18.1,
##     "cyl": 6,
##     "disp": 225,
##     "hp": 105,
##     "drat": 2.76,
##     "wt": 3.46,
##     "qsec": 20.22,
##     "vs": 1,
##     "am": 0,
##     "gear": 3,
##     "carb": 1,
##     "_row": "Valiant"
##   },
##   {
##     "mpg": 14.3,
##     "cyl": 8,
##     "disp": 360,
##     "hp": 245,
##     "drat": 3.21,
##     "wt": 3.57,
##     "qsec": 15.84,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 4,
##     "_row": "Duster 360"
##   },
##   {
##     "mpg": 24.4,
##     "cyl": 4,
##     "disp": 146.7,
##     "hp": 62,
##     "drat": 3.69,
##     "wt": 3.19,
##     "qsec": 20,
##     "vs": 1,
##     "am": 0,
##     "gear": 4,
##     "carb": 2,
##     "_row": "Merc 240D"
##   },
##   {
##     "mpg": 22.8,
##     "cyl": 4,
##     "disp": 140.8,
##     "hp": 95,
##     "drat": 3.92,
##     "wt": 3.15,
##     "qsec": 22.9,
##     "vs": 1,
##     "am": 0,
##     "gear": 4,
##     "carb": 2,
##     "_row": "Merc 230"
##   },
##   {
##     "mpg": 19.2,
##     "cyl": 6,
##     "disp": 167.6,
##     "hp": 123,
##     "drat": 3.92,
##     "wt": 3.44,
##     "qsec": 18.3,
##     "vs": 1,
##     "am": 0,
##     "gear": 4,
##     "carb": 4,
##     "_row": "Merc 280"
##   },
##   {
##     "mpg": 17.8,
##     "cyl": 6,
##     "disp": 167.6,
##     "hp": 123,
##     "drat": 3.92,
##     "wt": 3.44,
##     "qsec": 18.9,
##     "vs": 1,
##     "am": 0,
##     "gear": 4,
##     "carb": 4,
##     "_row": "Merc 280C"
##   },
##   {
##     "mpg": 16.4,
##     "cyl": 8,
##     "disp": 275.8,
##     "hp": 180,
##     "drat": 3.07,
##     "wt": 4.07,
##     "qsec": 17.4,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 3,
##     "_row": "Merc 450SE"
##   },
##   {
##     "mpg": 17.3,
##     "cyl": 8,
##     "disp": 275.8,
##     "hp": 180,
##     "drat": 3.07,
##     "wt": 3.73,
##     "qsec": 17.6,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 3,
##     "_row": "Merc 450SL"
##   },
##   {
##     "mpg": 15.2,
##     "cyl": 8,
##     "disp": 275.8,
##     "hp": 180,
##     "drat": 3.07,
##     "wt": 3.78,
##     "qsec": 18,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 3,
##     "_row": "Merc 450SLC"
##   },
##   {
##     "mpg": 10.4,
##     "cyl": 8,
##     "disp": 472,
##     "hp": 205,
##     "drat": 2.93,
##     "wt": 5.25,
##     "qsec": 17.98,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 4,
##     "_row": "Cadillac Fleetwood"
##   },
##   {
##     "mpg": 10.4,
##     "cyl": 8,
##     "disp": 460,
##     "hp": 215,
##     "drat": 3,
##     "wt": 5.424,
##     "qsec": 17.82,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 4,
##     "_row": "Lincoln Continental"
##   },
##   {
##     "mpg": 14.7,
##     "cyl": 8,
##     "disp": 440,
##     "hp": 230,
##     "drat": 3.23,
##     "wt": 5.345,
##     "qsec": 17.42,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 4,
##     "_row": "Chrysler Imperial"
##   },
##   {
##     "mpg": 32.4,
##     "cyl": 4,
##     "disp": 78.7,
##     "hp": 66,
##     "drat": 4.08,
##     "wt": 2.2,
##     "qsec": 19.47,
##     "vs": 1,
##     "am": 1,
##     "gear": 4,
##     "carb": 1,
##     "_row": "Fiat 128"
##   },
##   {
##     "mpg": 30.4,
##     "cyl": 4,
##     "disp": 75.7,
##     "hp": 52,
##     "drat": 4.93,
##     "wt": 1.615,
##     "qsec": 18.52,
##     "vs": 1,
##     "am": 1,
##     "gear": 4,
##     "carb": 2,
##     "_row": "Honda Civic"
##   },
##   {
##     "mpg": 33.9,
##     "cyl": 4,
##     "disp": 71.1,
##     "hp": 65,
##     "drat": 4.22,
##     "wt": 1.835,
##     "qsec": 19.9,
##     "vs": 1,
##     "am": 1,
##     "gear": 4,
##     "carb": 1,
##     "_row": "Toyota Corolla"
##   },
##   {
##     "mpg": 21.5,
##     "cyl": 4,
##     "disp": 120.1,
##     "hp": 97,
##     "drat": 3.7,
##     "wt": 2.465,
##     "qsec": 20.01,
##     "vs": 1,
##     "am": 0,
##     "gear": 3,
##     "carb": 1,
##     "_row": "Toyota Corona"
##   },
##   {
##     "mpg": 15.5,
##     "cyl": 8,
##     "disp": 318,
##     "hp": 150,
##     "drat": 2.76,
##     "wt": 3.52,
##     "qsec": 16.87,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 2,
##     "_row": "Dodge Challenger"
##   },
##   {
##     "mpg": 15.2,
##     "cyl": 8,
##     "disp": 304,
##     "hp": 150,
##     "drat": 3.15,
##     "wt": 3.435,
##     "qsec": 17.3,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 2,
##     "_row": "AMC Javelin"
##   },
##   {
##     "mpg": 13.3,
##     "cyl": 8,
##     "disp": 350,
##     "hp": 245,
##     "drat": 3.73,
##     "wt": 3.84,
##     "qsec": 15.41,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 4,
##     "_row": "Camaro Z28"
##   },
##   {
##     "mpg": 19.2,
##     "cyl": 8,
##     "disp": 400,
##     "hp": 175,
##     "drat": 3.08,
##     "wt": 3.845,
##     "qsec": 17.05,
##     "vs": 0,
##     "am": 0,
##     "gear": 3,
##     "carb": 2,
##     "_row": "Pontiac Firebird"
##   },
##   {
##     "mpg": 27.3,
##     "cyl": 4,
##     "disp": 79,
##     "hp": 66,
##     "drat": 4.08,
##     "wt": 1.935,
##     "qsec": 18.9,
##     "vs": 1,
##     "am": 1,
##     "gear": 4,
##     "carb": 1,
##     "_row": "Fiat X1-9"
##   },
##   {
##     "mpg": 26,
##     "cyl": 4,
##     "disp": 120.3,
##     "hp": 91,
##     "drat": 4.43,
##     "wt": 2.14,
##     "qsec": 16.7,
##     "vs": 0,
##     "am": 1,
##     "gear": 5,
##     "carb": 2,
##     "_row": "Porsche 914-2"
##   },
##   {
##     "mpg": 30.4,
##     "cyl": 4,
##     "disp": 95.1,
##     "hp": 113,
##     "drat": 3.77,
##     "wt": 1.513,
##     "qsec": 16.9,
##     "vs": 1,
##     "am": 1,
##     "gear": 5,
##     "carb": 2,
##     "_row": "Lotus Europa"
##   },
##   {
##     "mpg": 15.8,
##     "cyl": 8,
##     "disp": 351,
##     "hp": 264,
##     "drat": 4.22,
##     "wt": 3.17,
##     "qsec": 14.5,
##     "vs": 0,
##     "am": 1,
##     "gear": 5,
##     "carb": 4,
##     "_row": "Ford Pantera L"
##   },
##   {
##     "mpg": 19.7,
##     "cyl": 6,
##     "disp": 145,
##     "hp": 175,
##     "drat": 3.62,
##     "wt": 2.77,
##     "qsec": 15.5,
##     "vs": 0,
##     "am": 1,
##     "gear": 5,
##     "carb": 6,
##     "_row": "Ferrari Dino"
##   },
##   {
##     "mpg": 15,
##     "cyl": 8,
##     "disp": 301,
##     "hp": 335,
##     "drat": 3.54,
##     "wt": 3.57,
##     "qsec": 14.6,
##     "vs": 0,
##     "am": 1,
##     "gear": 5,
##     "carb": 8,
##     "_row": "Maserati Bora"
##   },
##   {
##     "mpg": 21.4,
##     "cyl": 4,
##     "disp": 121,
##     "hp": 109,
##     "drat": 4.11,
##     "wt": 2.78,
##     "qsec": 18.6,
##     "vs": 1,
##     "am": 1,
##     "gear": 4,
##     "carb": 2,
##     "_row": "Volvo 142E"
##   }
## ]

# Minify pretty_json: mini_json
mini_json <- minify(pretty_json)

# Print mini_json
mini_json

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Hopefully you agree that the pretty format is way easier to read and understand than the minified format!